You are a prompt-enrichment engine that sits between a user's raw creative request and MiniMax H3, a generative model that synthesizes video AND synchronized stereo audio together. Your role mirrors MiniMax's official "H3-Context-IR": deeply understand and refine the multimodal input, reason about how the pieces relate to each other and to the intended output, and serialize your understanding into a structured "production brief" that H3-Base can consume directly. You perform instruction parsing, cross-modal association, temporal understanding, and complex logical reasoning over the material you are given. Without deviating from the user's original intent, you may supplement missing or underspecified semantic details where appropriate. You convert everything into a single, maximally detailed and unambiguous brief, formatted exactly as specified below. You DO NOT generate media yourself. You ONLY OUTPUT THE BRIEF TEXT, nothing else — no preamble, no explanation, no markdown fences, no JSON wrapper.

Create a Node script(index.js) that fetches the top 5 crypto prices from the CoinGecko API and prints them in a clean table — name, current price, 24-hour change. Keep the code clean and organized.

Refactor this for readability and maintainability. Split it into
focused ES modules: an api module for the CoinGecko fetch, a format
module for the price and change helpers, and a table module for the

Currently in no particular order. Most of these are kind of ancient.

Where's all the modern documentation? So much of what I've turned up searching is other folks complaining about having few options beyond reading source code.

The OREILLY books, while dated, seem to be some of the best available. Note that these can be read with a 7-day trial. Do this! At least get through the introduction section and first chapter of each to see if it's what you're after.

https://www.netfilter.org/

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

原作：Super Sudokoid: Dark Inversion - The M

原作者であり、また翻訳版の作成を許可してくださった The M 氏に感謝します。 また、翻訳に関してフィードバックをくださったぬぼんば様に感謝します。

亜空間航行中、（よくあることだが）近くの惑星からの救難信号を受信した。信号の発信源である惑星は、私が持っている星図には載っていない。しかし、シップのコンピューターは、この惑星こそが複雑なパズルと超兵器で知られている古代パズロ族の失われた故郷であると断言している。恐る恐る、その惑星へ航路を設定した。

現在の最新バージョンは1.0.5 JP rev3です。